The DipInfo-UniTo system for SRST 2018
Valerio Basile, Alessandro Mazzei · 2018
This paper describes the system developed by the DipInfo-UniTo team to participate to the shallow track of the Surface Realization Shared Task 2018 (Mille et al., 2018).The system employs two separate neural networks with different architectures to predict the word ordering and the morphological inflection independently from each other.The UniTo realizer is language independent, and its simple architecture allowed it to be scored in the central part of the final ranking of the shared task.